ESW | R Documentation |
Returns effective strip width (ESW) for
line-transect detection functions.
See EDR
is for point transects.
ESW(object, newdata = NULL)
object |
An Rdistance model frame or fitted distance function,
normally produced by a call to |
newdata |
A data frame containing new values for
covariates at which either
ESW's or EDR's will be computed. If NULL and
|
ESW is the area under
the scaled distance function between its
left-truncation limit (obj$w.lo
) and its right-truncation
limit (obj$w.hi
).
If detection does not decline with distance,
the detection function is flat (horizontal), and
area under the detection
function is g(0)(w.hi - w.lo)
.
If, in this case, g(0) = 1
, effective sampling distance is
the half-width of the surveys, (w.hi - w.lo)
If newdata
is present, the returned value is
a vector of effective sampling distances for values of the
covariates in newdata
with length equal to
the number of rows in newdata
.
If newdata
is NULL, the returned value is a vector of effective
sampling distances associated with covariate values in object
and has
the same number of detected groups. The returned vector
has measurement units, i.e., object$outputUnits
.
Rdistance uses Simpson's composite 1/3 rule to numerically
integrate under distance functions. The number of points evaluated
during numerical integration is controlled by
options(Rdistance_intEvalPts)
(default 101).
Option 'Rdistance_intEvalPts' must be odd because Simpson's rule
requires an even number of intervals (hence, odd number of points).
Lower values of 'Rdistance_intEvalPts' increase calculation speeds;
but, decrease accuracy.
'Rdistance_intEvalPts' must be >= 5. A warning is thrown if
'Rdistance_intEvalPts' < 29. Empirical tests by the author
suggest 'Rdistance_intEvalPts' values >= 30 are accurate
to several decimal points and that all 'Rdistance_intEvalPts' >= 101 produce
identical results in all but pathological cases.
dfuncEstim
, EDR
,
effectiveDistance
data(sparrowDf)
dfunc <- sparrowDf |> dfuncEstim(formula=dist~bare)
ESW(dfunc) # vector length 356 = number of groups
ESW(dfunc, newdata = data.frame(bare = c(30,40))) # vector length 2
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